Renal cell cancer is an intractable disease with low response rates to any of the therapies presently practiced, such as pharmacotherapy using chemotherapeutic drugs, immunotherapeutic drugs, etc., radiotherapy, surgical operation, and/or the like. Further, by the time renal cell cancer is identified, cases that have already developed distant metastasis are reportedly as high as about 30%. Excluding surgical operation, immunotherapy using interferons (IFNs) among the above pharmacotherapies, is particularly considered most effective; however, response rates attained by such a therapy are as low as only about 15% when IFN-α is administered singly, and 10 to 15% when IFN-γ is administered singly. Even when used in combination with anti-cancer agents, there is no other therapy that provides higher effect than that of the IFN-α monotherapy. The currently practiced immunotherapy using IFN is primarily a long-term maintenance therapy with single use of IFN-α, or combined use with IFN-γ.
Meanwhile, progress of genome sciences are rapidly elucidating pharmacokinetics, and gene polymorphisms of enzymes, proteins, etc., which are relevant to drug responsibility. In human genome analysis, single nucleotide polymorphisms (SNPs) have been receiving attention since gene polymorphism markers are found most frequently. Such SNPs are known to have been useful for analyzing human genome relevant to common diseases, drug responses, etc. (see non-patent documents 1, 2 and 3). Haplotype analysis using a plurality of SNPs is also known to have been useful for analyzing the susceptibility to diseases (see non-patent document 4).
Lately, to establish so-called order-made medicine for individual patients, studies to reveal a relationship between a given gene polymorphism and drug sensitivity/drug responsibility of a patient have been proposed.
A known method for predicting the effects of IFN therapy analyzes the relationship of MxA-8/MxA123 MBL-221/MBL-CLDcodon54 SNPs on the genome of an HCV (human hepatitis C virus (human HCV))-infected patient with IFN-α responders (patients responsive to the therapy) and non-responders (patients not responsive to the therapy), thereby predicting and evaluating the degree of IFN treatment response of the HCV-infected patient (patent document 1). Another known method predicts IFN treatment response using SNP in the promoter region and at position 134 of IFN-α receptor 2 gene, as gene polymorphism markers (patent document 2). This document discloses target diseases selected from the group consisting of hepatitis B, hepatitis C, glioblastoma, medulloblastoma, astrocytoma, skin malignant melanoma, and like hepatitis, renal cancers, multiple myeloma, hairy cell leukemia, chronic myelogenous leukemia, subacute sclerosing panencephalitis, virus encephalitis, systemic zoster and varicella of a patient with immunosuppression, epipharynx anaplastic carcinoma, viral internal ear infectious disease accompanied by hypacusis, herpetic keratitis, flat condyloma, condyloma acuminatum, conjunctivitis caused by adenovirus and/or herpesvirus infection, genital herpes, cold sore, uterine cervix carcinoma, cancerous hydrothorax, keratoacanthoma, basal cell carcinoma, and chronic active hepatitis δ.
Further, to evaluate response of IFN-α therapy for hepatitis C, an also known method is to measure substitution from guanine (G) to adenine (A) at position 196 in the promoter region of IRF-1 gene (see patent document 3).
To date, however, there has been no document specifically reporting a relationship between IFN therapy response to renal cell cancers and given SNPs.
For IFN-α responders and non-responders, there have already been several hundred gene expression profiles reported (see non-patent document 4 and patent document 4). These profiles were created using technologies such as DNA chips (high density oligonucleotides or microarrays), differential display, differential cDNA arrays, SAGE (serial analysis of gene expression), expressed sequence tag database comparison, or the like. These methods were used to analyze gene expression in colon, breast, ovarian carcinomas, multiple sclerosis lesions, leukemia, and renal cell carcinomas. Non-patent document 4 further includes genes such as IRF2, STAT1, STAT2, STAT4, STAT5, STAT6, etc.; however, no SNPs of specific genes used in the present invention are disclosed.    [Patent document 1] Unexamined Japanese Patent Publication No. 2003-88382    [Patent document 2] Unexamined Japanese Patent Publication No. 2003-339380    [Patent document 3] Unexamined Japanese Patent Publication No. 2001-136973    [Patent document 4] Unexamined Japanese Patent Publication No. 2004-507253    [Non-patent document 1] Brookes, A. J., “The essence of SNPs”, Gene, USA, (1999), 234, 177-186    [Non-patent document 2] Cargill, M, et al., “Characterization of single-nucleotide polymorphisms in coding regions of human genes”, Nature Genet., USA, (1999), 22, 231-238    [Non-patent document 3] Evans, W. E., & Relling, M. V., “Pharmacogenomics: translating functional genomics into rational therapeutics”, Science, USA, (1999), 286, 487-491    [Non-patent document 4] Schlaak, J. F., et. al., “Cell-type and Donor-specific Transcriptional Responses to Interferon-α”, J. Biol. Chem., (2002) 277, 51, 49428-49437